Method For Calculation Of Density Research Articles

Forest point cloud denoising method based on curvature and density

ABSTRACT When LiDAR scans woodlands, the three-dimensional (3D) point cloud data of woodlands directly collected usually contain a large number of noise points, which is caused by equipment performance, environmental interference, or other factors. The poor data quality of the point cloud data affects the accuracy of the extracted individual tree feature parameters and forest 3D reconstruction. To improve the quality of forest 3D point cloud data, a curvature- and density-based denoising method has been proposed in this study. First, the surface curvature features were calculated for the noise-dense trunk area of the forest for primary denoising, and the initial denoising results were used to construct the K-dimensional-Tree (KD-Tree) space index to search the spherical neighbourhood. The denoising was completed according to the proposed density calculation method (SN-NN, Spherical Neighbourhood and Nearest Neighbour). To evaluate the comprehensive performance of the proposed denoising method, a new denoising evaluation index has been designed by integrating the denoising precision and effective data retention degree of the forest point cloud data. The proposed method was validated based on forest point cloud data collected using Terrestrial LiDAR. The results show that the denoising precision and effective data retention degree of the proposed method are both preferable, which can effectively improve the quality of forest point cloud data.

Calculation method and evolution rule of the strain energy density of sandstone under true triaxial compression

Deep rock masses are typically in complex stress states, and research on the evolution of their strain energy density is of highly important for understanding their failure characteristics. In this work, a true triaxial stress‒balanced unloading test is designed to analyze the ud and ue evolution of sandstone under true triaxial compression conditions. The study results indicate that as σ1 increases, the elastic strain decreases in the σ2 and σ3 directions, whereas the residual strain progressively increases, and the magnitude of decrease in elastic strain exceeds the magnitude of increase in residual strain. Throughout the loading process of σ1, ue progressively decreases in the σ2 and σ3 directions, whereas ud gradually increases, and the magnitude of decrease in ue surpasses the magnitude of increase in ud. The ud and ue of sandstone under different stress levels were calculated via true triaxial stress‒balanced unloading tests, and the evolution of ud and ue in the three principal stress directions and the overall strain energy density of sandstone followed a linear energy storage law. On the basis of this law and the true triaxial stress‒balanced unloading test, a new method for calculating the true triaxial ud and ue was proposed. A study on the σ1 unloading stress path revealed that the σ1 unloading stress path significantly affects the storage and dissipation of the strain energy density in the three principal stress directions of sandstone. On the basis of the research results, the criteria for determining rockbursts were discussed.

A robust assessment method of point cloud quality for enhancing 3D robotic scanning

Point clouds are widely used to construct models of workpieces using 3D scanners, especially where high-quality robotic and automatic 3D scanning is required in industries and manufacturing. In recent years, Point Cloud Quality Assessment (PCQA) has garnered increasing attention as it provides quality scores for entire point clouds, addressing issues such as downsampling and compression distortions. However, current PCQA methods cannot provide specific and local quality scores, which are necessary to facilitate rescanning and recompletion in 3D robotic scanning. Additionally, 3D data produced by robotic view-planning algorithms are usually considered the final result, where PCQA is typically not involved. In this paper, we bridge the gap between PCQA methods and practical robotic 3D scanning. We propose a no-reference PCQA method that recognizes sparse regions during 3D scanning, providing both local and overall quality scores. Unlike traditional methods that primarily consider density as a key metric, our method assumes that an expected 3D scan will have a uniformly distributed point cloud on surfaces. We analyze the quality of points by using geometric information from surfaces fitted to these points, which are mapped to a 2D distribution based on specified distances and angles. We conducted experiments on various datasets, including both synthetic and public datasets, to evaluate the accuracy and robustness of our method. The results show that our method can represent the quality on surfaces more accurately and robustly than density calculation methods. Additionally, it outperforms most existing PCQA methods in scenarios of downsampling, which is a common challenge in high-quality 3D scanning applications. The performance of our quality enhancement experiments on practical 3D scanning, conducted towards the end of our study, demonstrates significant potential for real-world applications. The related code is released at https://github.com/leihui6/PQA.

Effect of true triaxial principal stress unloading rate on strain energy density of sandstone

Deep rock are often in a true triaxial stress state. Studying the impacts of varying unloading speeds on their strain energy (SE) density is highly significant for predicting rock stability. Through true triaxial unloading principal stress experiments and true triaxial stress equilibrium unloading experiments on sandstone, this paper proposes a method to compute the SE density in a true triaxial compressive unloading principal stress test. This method aims to analyze the SE variation in rocks under the action of true triaxial unloading principal stresses. Acoustic emission is used to verify the correctness of the SE density calculation method in this paper. This study found that: (1) Unloading in one principal stress direction causes the SE density to rise in the other principal stress directions. This rise in SE, depending on its reversibility, can be categorized into elastic and dissipated SE. (2)When unloading principal stresses, the released elastic SE density in the unloading direction is influence by the stress path and rate. (3) The higher the unloading speed will leads to greater increases in the input SE density, elastic SE density, and dissipative SE density in the other principal stress directions. (4) The dissipated SE generated under true triaxial compression by unloading the principal stress is positively correlated with the damage to the rock; with an increase in unloading rate, there is a corresponding increase in the formation of cracks after unloading. (5) Utilizing the stress balance unloading test, we propose a calculation method for SE density in true triaxial unloading principal stress tests.

Motor magnetic field analysis using the edge-based smooth finite element method (ES-FEM)

This paper proposes a smooth finite element method (S-FEM) for efficient and accurate analysis of motor magnetic fields. The edge-based smooth finite element (ES-FEM) formulations are derived for two-dimensional triangular element meshes suitable for multi-material motor structures, and then a magnetic flux density calculation method appropriate for S-FEM to calculate nonlinear electromagnetic fields is introduced. In the calculation of motor magnetic field, ES-FEM has a proper softening effect on the stiffness matrix of the finite elements, which makes the calculation more accurate and more efficient than FEM. The magnetic flux intensity calculation method introduced in this paper is not only appropriate for ES-FEM to calculate the nonlinear electromagnetic field of motor, but also can improve the calculation accuracy of magnetic flux density. In the calculation of nonlinear magnetic field of motor, ES-FEM can also improve the calculation accuracy of magnetic field analysis compared with FEM. Several numerical examples demonstrate the improving effect of the ES-FEM discussed above on the motor's magnetic field calculation.

Exploration of the comparison method between nuclear magnetic logging porosity and core detection porosity

Porosity is the basis of reservoir parameter interpretation, permeability and saturation parameters are closely related to porosity, and the interpretation model of porosity has been established with the correlation between core porosity and electrical properties, and core porosity is the effective porosity measured by applying liquid saturation method. With the application of nuclear magnetic logging technology, the interpretation of porosity is more refined and can be subdivided into total porosity, effective porosity, capillary-bound water porosity, and clay-bound water porosity. In order to apply the nuclear magnetic logging technology more accurately, it is necessary to find a method to compare the two porosity detection methods. In this paper, we discuss the meaning of core porosity through the operation procedure of core detection porosity, combined with core piezometric curve and core density calculation method, get the conclusion that core detection porosity approximates total porosity, explore the comparison method between total porosity and core porosity of nuclear magnetic logging, and lay the geological foundation for the next step of applying nuclear magnetic logging data to improve porosity interpretation.

Absorption Spectra as Predictors of Algal Biomass and Pigment Content of the Cultured Microalgae Amphidinium carterae, Isochrysis galbana, Nephroselmis sp., and Anabaena sp.

Background: In the search of a rapid and representative method for the approximate calculation of culture density and cell content of useful pigments, the study of absorption spectra of cultures of the dinoflagellate Amphidinium carterae, the haptophyte Isochrysis galbana, the chlorophyte Nephroselmis sp., and the filamentous cyanobacterium Anabaena sp. were selected as representative species of different taxa. Methods: The experimental cultures were established in small volumes by the discontinuous method under 20–21 °C, salinity of 30 or 40 ppt, and 2000 or 8000 lux illumination, and the absorbance spectra, density of the culture and concentration of the extracted pigments chlorophyll, total carotenoids, and phycocyanin were recorded during cultivation. Results: Algal density can be predicted sufficiently correctly because the regression equation of the correlation of the OD value of 750 nm from each absorption spectrum and the measured algal biomass was very strong. The same is true for the corresponding correlations between OD 750 nm and the detected pigments. Conclusions: Absorption spectra of microalgal cultures can be a simple, inexpensive, and non-invasive method for the growers to obtain the necessary information for predicting the right time to collect an ideal combination of maximum biomass and useful pigments, provided that the interpretation of the spectra is performed according to the method described herein.

Logistics density, E-commerce and high-quality economic development: An empirical analysis based on provincial panel data in China

Under the background of high-quality development, the e-commerce industry needs to seek more comprehensive development, and the logistics industry should not only maintain higher efficiency, but also meet the requirements of green and low-carbon transportation. This study aims to investigate the link between the logistic density and the proportion of e-commerce transaction scale. There are few studies on how logistics density affects the development of e-commerce quantitatively. In order to solve this gap, this study establishes an impact model of high-quality growth of e-commerce, and based on Solow production model, constructs a calculation method of logistics density, and analyzes the impact mechanism of logistics density on the scale ratio of e-commerce transactions. Using the bidirectional fixed effect model, this paper makes an empirical analysis of the panel data of 31 provinces consisting of 248 observations and autonomous regions in China from 2014 to 2020. The results show that logistic density has a positive and significant impact on the growth of e-commerce, thus promoting the optimization of economic structure. The acceleration of logistics density decreases the expansion of the ecommerce. Finally, there are obvious regional differences between the development of e-commerce and logistics infrastructure in China. Finally, based on the empirical result, this paper puts forward recommendations for government and companies.

Public emotional atmosphere during disasters: understanding emotions in short video comments on the Zhengzhou flood

Short videos are gaining popularity among Chinese netizens who use them to socialize and express themselves online, particularly during disasters. This study examines the characteristics of an emotional atmosphere through short video comments. We developed the Multimodal Social Short Video Crawler V1.0 to analyze 157,747 comments from 343 short videos about the “Zhengzhou flood.” First, we categorized comments into clusters and calculated cluster density using complex network analysis methods. Second, we employed computer-mediated content analysis to derive sentiment values in clusters. To capture the emotional atmosphere in short videos accurately, we developed a trust model to assign weights to both cluster density and sentiment value. Our findings reveal that the emotional atmosphere in short video comments indicates continuity and clustering traits. Furthermore, a correlation was found between cluster size and emotional valence, with the relationship trend varying between macro- and micro-clusters. Regarding theoretical contributions, this study enriches the theory of emotional atmosphere by proposing a mathematical model of emotional atmosphere that combines machine learning and complex network analysis, and introduces cluster density and emotional value calculation methods. Concurrently, this study highlights emotion’s importance in short video comments, which can enhance disaster response efforts in the future.

Revisiting ‘revisiting supervised methods for effort‐aware cross‐project defect prediction’

AbstractEffort‐aware cross‐project defect prediction (EACPDP), which uses cross‐project software modules to build a model to rank within‐project software modules based on the defect density, has been suggested to allocate limited testing resource efficiently. Recently, Ni et al. proposed an EACPDP method called EASC, which used all cross‐project modules to train a model without considering the data distribution difference between cross‐project and within‐project data. In addition, Ni et al. employed the different defect density calculation strategies when comparing EASC and baseline methods. To explore the effective defect density calculation strategies and methods on EACPDP, the authors compare four data filtering methods and five transfer learning methods with EASC using four commonly used defect density calculation strategies. The authors use three classification evaluation metrics and seven effort‐aware metrics to assess the performance of methods on 11 PROMISE datasets comprehensively. The results show that (1) The classification before sorting (CBS+) defect density calculation strategy achieves the best overall performance. (2) Using balanced distribution adaption (BDA) and joint distribution adaptation (JDA) with the K‐nearest neighbour classifier to build the EACPDP model can find 15% and 14.3% more defective modules and 11.6% and 8.9% more defects while achieving the acceptable initial false alarms (IFA). (3) Better comprehensive classification performance of the methods can bring better EACPDP performance to some extent. (4) A flexible adjustment of the defect threshold λ of the CBS+ strategy contribute to different goals. In summary, the authors recommend researchers and practitioners use to BDA and JDA with the CBS+ strategy to build the EACPDP model.

Density peaks clustering based on local fair density and fuzzy k-nearest neighbors membership allocation strategy

The density peaks clustering algorithm (DPC) has been widely concerned since it was proposed in 2014. There is no need to specify in advance and only one parameter required. However, some disadvantages are still witnessed in DPC: (1) Requiring repeated experiments for choosing a suitable calculation method of the local density due to the variations in the scale of the dataset, which will lead to additional time cost. (2) Difficulty in finding an optimal cutoff distance threshold, since different parameters not only impact the selection of cluster centers but also directly affect the quality of clusters. (3) Poor fault tolerance of the allocation strategy, especially in manifold datasets or datasets with uneven density distribution. Targetting solutions to these problems, a density peaks clustering based on local fair density and fuzzy k-nearest neighbors membership allocation strategy (LF-DPC) is proposed in this paper. First, to obtain a more balanced local density, two classic local density calculation methods are combined in the algorithm to calculate the local fair density through the optimization function with the smallest local density difference. Second, a robust two stage remaining points allocation strategy is designed. In the first stage, k-nearest neighbors are used to quickly and accurately allocate points from the cluster center. In the second stage, to further improve the accuracy of allocation, a fuzzy k-nearest neighbors membership method is designed to allocate the remaining points. Finally, the LF-DPC algorithm has been experimented based on several synthetic and real-world datasets. The results prove that the proposed algorithm has obvious advantages compared with the other five ones.

Cooperative effect of Mg and Si contents on the microstructural evolution, mechanical performance, and deformation behavior of cast Al–Li–Mg–Si alloys

In this study, the cooperative effect of Mg and Si contents on the microstructural evolution, mechanical properties, and deformation behavior of the cast Al–Li–Mg–Si alloys is investigated. Five different Al–Li–Mg–Si alloys are prepared by conventional casting method, which are referred to as Alloy A-E. The results show that with the increase in the Mg content, the amount of lath-like Mg2Si precipitates and bulk-like AlLiSi particles increases, which leads to an improvement in the strength and ductility of alloys. The amount of intergranular Si-rich phases in Alloy D increases with the increase in the Si content. Further, a higher Si content in Alloy E promotes the formation of bulk-like AlLiSi. The geometric phase analysis (GPA) results reveal strain partitioning behavior at the interface of Mg2Si precipitates and AlLiSi particles, which follows the Orowan looping or dislocation cutting strengthening mechanism. The geometrically necessary dislocation (GND) density calculation results suggest that the lath-like Mg2Si precipitates play a critical role in the deformation behaviors of Alloy A-C. The hindering effect of lath-like Mg2Si precipitates on the movement of dislocations decreases the GND density gap between high-angle grain boundaries or junctions and grain interior and improves the resistance to deformation. However, the deformation behaviors of Alloy D and E with higher Si content are mainly governed by the intergranular Si-rich phases. Finally, a methodological framework is established to elucidate the effect of precipitates and/or intergranular phases on the deformation behaviors of materials using a combination of GPA and GND density calculation methods.

Development and experimental validation of a fast and accurate field calculation tool for axial flux permanent magnet machines

A fast calculation method of magnetic flux density in axial flux permanent magnet (AFPM) machine is presented. This method is based on an original combination of magnetic charges concept, image theory, and relative permeance functions. Using this combined approach, significant 3D effects, which are present in axial flux machines such as curvature and edge effects as well as slotting effects due to stator slots, are considered. The main electromagnetic specifications of the AFPM machine such as cogging torque, Back Electromotive Force (EMF), and local air-gap flux density are calculated accurately in very short times. To validate this approach, an original prototype of double stator ironless rotor AFPM machine with adjustable air-gap is used. Experimental protocol including measurements of global characteristics (EMF, Torque) and measurements of local field density is presented. The obtained results are in good agreement with the experimental measurements which allows validating the proposed computation method.

A novel data cluster algorithm based on linear regression and residual analysis for human resource management

Human resource management has become an important part of enterprise management. How to select high-quality talent and how to allocate corresponding talent to appropriate work has become an increasingly acute problem. Traditional data cluster methods cannot effectively solve the above problem due to the high-dimensional data. Therefore, we propose a novel data cluster algorithm based on linear regression and residual analysis for human resource management. Improved hybrid entropy weight attribute similarity is adopted for measuring the similarity between objects. The proposed local density calculation method based on k-nearest neighbour (KNN) and Parzen window is used to calculate the density of each object. Then, we utilise the linear regression and residual analysis to select the clustering centre points quickly and automatically, which can eliminate the subjectivity of artificial selection. A new clustering centre objective optimisation model is proposed to determine the real clustering centre. Through theoretical analysis and comparative experiments on artificial data sets and real data sets, it shows that the proposed cluster algorithm can overcome the defects of the original algorithms, and achieve better clustering effect and lower computation time than state-of-the-art methods.

An improved density-based approach to risk assessment on railway investment

PurposeDensity-based spatial clustering of applications with noise (DBSCAN) is the most commonly used density-based clustering algorithm, while it cannot be directly applied to the railway investment risk assessment. To overcome the shortcomings of calculation method and parameter limits of DBSCAN, this paper proposes a new algorithm called Improved Multiple Density-based Spatial clustering of Applications with Noise (IM-DBSCAN) based on the DBSCAN and rough set theory.Design/methodology/approachFirst, the authors develop an improved affinity propagation (AP) algorithm, which is then combined with the DBSCAN (hereinafter referred to as AP-DBSCAN for short) to improve the parameter setting and efficiency of the DBSCAN. Second, the IM-DBSCAN algorithm, which consists of the AP-DBSCAN and a modified rough set, is designed to investigate the railway investment risk. Finally, the IM-DBSCAN algorithm is tested on the China–Laos railway's investment risk assessment, and its performance is compared with other related algorithms.FindingsThe IM-DBSCAN algorithm is implemented on China–Laos railway's investment risk assessment and compares with other related algorithms. The clustering results validate that the AP-DBSCAN algorithm is feasible and efficient in terms of clustering accuracy and operating time. In addition, the experimental results also indicate that the IM-DBSCAN algorithm can be used as an effective method for the prospective risk assessment in railway investment.Originality/valueThis study proposes IM-DBSCAN algorithm that consists of the AP-DBSCAN and a modified rough set to study the railway investment risk. Different from the existing clustering algorithms, AP-DBSCAN put forward the density calculation method to simplify the process of optimizing DBSCAN parameters. Instead of using Euclidean distance approach, the cutoff distance method is introduced to improve the similarity measure for optimizing the parameters. The developed AP-DBSCAN is used to classify the China–Laos railway's investment risk indicators more accurately. Combined with a modified rough set, the IM-DBSCAN algorithm is proposed to analyze the railway investment risk assessment. The contributions of this study can be summarized as follows: (1) Based on AP, DBSCAN, an integrated methodology AP-DBSCAN, which considers improving the parameter setting and efficiency, is proposed to classify railway risk indicators. (2) As AP-DBSCAN is a risk classification model rather than a risk calculation model, an IM-DBSCAN algorithm that consists of the AP-DBSCAN and a modified rough set is proposed to assess the railway investment risk. (3) Taking the China–Laos railway as a real-life case study, the effectiveness and superiority of the proposed IM-DBSCAN algorithm are verified through a set of experiments compared with other state-of-the-art algorithms.

Energy distribution and effective components analysis of 2n sequence pseudo-random signal

In order to extract usable harmonics from real 2n sequence pseudo-random data, a technical method is proposed. An equation for predicting the average amplitude of the main frequencies is proposed to guide the choice of signal type for different exploration tasks. By the threshold of the amplitude of the transmitted signal, a set of candidate frequencies are first selected. Then, by operating a spectrum envelope method at these candidate frequencies on received data, effective components in data are extracted. A frequency density calculation method is proposed based on a logical number summation method, to reasonably characterize the frequency density in different frequency bands. By applying this method to real data in Sichuan, China, with signal Type 13, 75 effective components are extracted, including both main frequencies and harmonics. The result suggests that the number of effective frequencies in the 2n sequence pseudo-random signal can be increased by extracting usable harmonics, without any additional fieldwork.

A stable community detection approach for complex network based on density peak clustering and label propagation

Dividing a network into communities has great benefits in understanding the characteristics of the network. The label propagation algorithm (LPA) is a fast and convenient community detection algorithm. However, the community initialization of LPA does not take advantage of topological information of networks, and its robustness is poor. In this paper, we propose a stable community detection algorithm based on density peak clustering and label propagation (DS-LPA). First, the local density calculation method in density peak clustering algorithm is improved in finding the community center of the network, so as to build a suitable initial community, which can improve the quality of community partition. Then, the label update order is determined reasonably by computing the information transmission power of nodes, and the solutions for multiple candidate labels are provided, which greatly improved the robustness of the algorithm. DS-LPA is compared with other seven algorithms on the synthetic network and real-world networks. NMI, ARI, and modularity are used to evaluate these algorithms. It can be concluded that DS-LPA has a higher performance than most comparison algorithms on synthetic network with ten different mixed parameters by statistical testing. And DS-LPA can quickly calculate the best community partition on different sizes of real-world networks.

Perbandingan Estimator Histogram dan Estimator Kernel

Salah satu hal penting dalam analisis statistik adalah prosedur estimasi suatu fungsi padat peluang yang biasa disebut fungsi densitas. Ada dua metode pendekatan yang biasanya digunakan, yaitu pendekatan parameter yang terkait dengan asumsi distribusi tertentu dan metode estimasi densitas secara non parametrik. Metode non parametrik yang sering kita jumpai adalah metode histogram.
 Beberapa kelemahan metode histogram menjadi acuan untuk dikembangkannya metode yang lain yaitu metode kernel, dimana estimator densitas kernel ini mempunyai parameter yang perlu diestimasi yaitu bandwidth h. Yang menjadi rumusan masalah dalam kajian pustaka ini adalah bagaimana memilih bandwidth dari estimator densitas kernel pada suatu fungsi densitas f di R dan membandingkannya dengan estimator histogram.
 Kesimpulan yang dapat diambil antara lain: (1) Estimasi densitas histogram adalah:
 dengan , (2) Estimator densitas kernel adalah :
 , (3) Estimator kernel mengatasi kelemahan- kelemahan dari estimator histogram, (4) Tingkat konvergensi dari estimator kernel lebih baik dari pada histogram, (5) Pemilihan bandwidth dengan metode unbiased cross validation (least cross validation) secara asymtotik menghasilkan bandwidth yang optimum

Breast density calculation method using deep learning

［目的］MG における乳房構成は，わが国では目視による定性的判定が主流だが，観察者間や機種間で生じる判定の不一致が課題である。われわれは乳房構成の自動判定化を目標に２０１７年より研究を開始した。２０１９年に考案した脂肪領域を基準とする乳腺濃度算出法は複数機種にて一定の精度を得たが，過度な処理を施した画像など一部に対して精度にばらつきが生じた。今回新たに脂肪領域と大胸筋を基準とする手法を検討した。 ［方法］複数機種のMLO画像が混在した３００例の乳腺領域／脂肪領域／大胸筋のマスキング画像を作成し，AIのアルゴリズムである U-Netモデルで学習させ，３領域の自動抽出を可能にした。大胸筋と脂肪の画素値から補正関数を求め，乳腺濃度を算出するための画像を生成し，それを基に大胸筋と同等以上の相対画素値の割合を乳腺濃度として算出した。学習用とは別の６機種のMGを１，０００例用意し，乳腺濃度算出値と目視判定の関係について機種別にROC解析を行い，判定の一致度をKappa（κ）係数で調べた。また１，０００例中，同一乳房を異なる機種で撮影したことのある７７８例を３８９のペア画像とし，機種間の算出値の相関を調べた。 ［結果・考察］AUC 値は６機種いずれも０．８０以上，高濃度群／非高濃度群のκ係数は０．６１，４項目における重み付けκ係数は０．９６であった。相関係数は全ての組み合わせで０．７０以上であった。今後AIを活用した乳房構成の自動判定化が実現できれば，判定の統一化や読影医の労力軽減の一助として貢献できると思われた。

Fast Searching Density Peak Clustering Algorithm Based on Shared Nearest Neighbor and Adaptive Clustering Center

The clustering analysis algorithm is used to reveal the internal relationships among the data without prior knowledge and to further gather some data with common attributes into a group. In order to solve the problem that the existing algorithms always need prior knowledge, we proposed a fast searching density peak clustering algorithm based on the shared nearest neighbor and adaptive clustering center (DPC-SNNACC) algorithm. It can automatically ascertain the number of knee points in the decision graph according to the characteristics of different datasets, and further determine the number of clustering centers without human intervention. First, an improved calculation method of local density based on the symmetric distance matrix was proposed. Then, the position of knee point was obtained by calculating the change in the difference between decision values. Finally, the experimental and comparative evaluation of several datasets from diverse domains established the viability of the DPC-SNNACC algorithm.